---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-ner
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-base-finetuned-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1738
- Precision: 0.6666
- Recall: 0.7036
- F1: 0.6846
- Accuracy: 0.6664

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.19  | 50   | 1.3378          | 0.2046    | 0.2159 | 0.2101 | 0.2062   |
| No log        | 0.37  | 100  | 1.3271          | 0.2419    | 0.2553 | 0.2484 | 0.2431   |
| No log        | 0.56  | 150  | 1.3164          | 0.2741    | 0.2893 | 0.2815 | 0.2753   |
| No log        | 0.75  | 200  | 1.3061          | 0.3090    | 0.3261 | 0.3173 | 0.3100   |
| No log        | 0.93  | 250  | 1.2965          | 0.3373    | 0.3560 | 0.3464 | 0.3381   |
| No log        | 1.12  | 300  | 1.2872          | 0.3726    | 0.3932 | 0.3826 | 0.3734   |
| No log        | 1.31  | 350  | 1.2783          | 0.4027    | 0.4251 | 0.4136 | 0.4034   |
| No log        | 1.49  | 400  | 1.2697          | 0.4327    | 0.4567 | 0.4444 | 0.4333   |
| No log        | 1.68  | 450  | 1.2613          | 0.4565    | 0.4818 | 0.4688 | 0.4569   |
| 1.2812        | 1.87  | 500  | 1.2537          | 0.4768    | 0.5032 | 0.4897 | 0.4774   |
| 1.2812        | 2.05  | 550  | 1.2464          | 0.4971    | 0.5247 | 0.5105 | 0.4975   |
| 1.2812        | 2.24  | 600  | 1.2394          | 0.5185    | 0.5472 | 0.5324 | 0.5189   |
| 1.2812        | 2.43  | 650  | 1.2328          | 0.5341    | 0.5637 | 0.5485 | 0.5345   |
| 1.2812        | 2.61  | 700  | 1.2266          | 0.5480    | 0.5784 | 0.5628 | 0.5484   |
| 1.2812        | 2.8   | 750  | 1.2208          | 0.5630    | 0.5942 | 0.5782 | 0.5634   |
| 1.2812        | 2.99  | 800  | 1.2153          | 0.5771    | 0.6091 | 0.5927 | 0.5773   |
| 1.2812        | 3.17  | 850  | 1.2100          | 0.5903    | 0.6230 | 0.6062 | 0.5905   |
| 1.2812        | 3.36  | 900  | 1.2051          | 0.5993    | 0.6325 | 0.6155 | 0.5995   |
| 1.2812        | 3.54  | 950  | 1.2008          | 0.6128    | 0.6468 | 0.6294 | 0.6128   |
| 1.2012        | 3.73  | 1000 | 1.1967          | 0.6202    | 0.6546 | 0.6370 | 0.6200   |
| 1.2012        | 3.92  | 1050 | 1.1931          | 0.6264    | 0.6611 | 0.6433 | 0.6262   |
| 1.2012        | 4.1   | 1100 | 1.1896          | 0.6352    | 0.6704 | 0.6523 | 0.6350   |
| 1.2012        | 4.29  | 1150 | 1.1865          | 0.6426    | 0.6782 | 0.6599 | 0.6424   |
| 1.2012        | 4.48  | 1200 | 1.1838          | 0.6467    | 0.6825 | 0.6641 | 0.6465   |
| 1.2012        | 4.66  | 1250 | 1.1814          | 0.6529    | 0.6890 | 0.6705 | 0.6526   |
| 1.2012        | 4.85  | 1300 | 1.1794          | 0.6568    | 0.6932 | 0.6745 | 0.6565   |
| 1.2012        | 5.04  | 1350 | 1.1777          | 0.6598    | 0.6964 | 0.6776 | 0.6596   |
| 1.2012        | 5.22  | 1400 | 1.1763          | 0.6617    | 0.6984 | 0.6795 | 0.6615   |
| 1.2012        | 5.41  | 1450 | 1.1752          | 0.6635    | 0.7003 | 0.6814 | 0.6633   |
| 1.1618        | 5.6   | 1500 | 1.1744          | 0.6652    | 0.7020 | 0.6831 | 0.6650   |
| 1.1618        | 5.78  | 1550 | 1.1740          | 0.6660    | 0.7029 | 0.6839 | 0.6658   |
| 1.1618        | 5.97  | 1600 | 1.1738          | 0.6666    | 0.7036 | 0.6846 | 0.6664   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2